In this Power BI project, I analyzed a year’s worth of sales data from a fictitious pizza place. The dataset includes comprehensive details on order times, pizza types, sizes, quantities, prices, and ingredients.
- Analyze daily customer counts and identify peak hours.
- Determine the average number of pizzas per order and identify bestsellers.
- Assess total annual revenue and uncover any seasonal sales patterns.
- Evaluate menu performance and recommend promotional strategies.
- Utilized Power BI Desktop to import the sales data from Excel/CSV files.
- Cleaned the dataset using Power Query Editor, addressing missing values, duplicates, and ensuring correct data types.
- Applied DAX (Data Analysis Expressions) to create key metrics such as total orders, total revenue, etc.
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Designed interactive dashboards to visualize sales trends and peak hours.
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pizza popularity.
- Sales Trends: Notable peak in July, decline in September and October, with a rebound in November.
- Peak Hours: Highest activity between 12:00-13:00 and 17:00-18:00.
- Top Pizzas: Large-sized pizzas are most popular; Classic Deluxe leads in sales, while Thai Chicken generates highest revenue.
- Busiest Day: Fridays, with peak sales reaching 3,500 orders.
- Low Performers: Brie Carre Pizza shows the lowest sales and revenue.
- Adjust staffing to cover peak hours more effectively.
- Promote large-sized pizzas and the Classic category.
- Prepare for seasonal sales changes.
- Ensure adequate inventory for busy Fridays.
This project highlights my expertise in data analysis, Power BI visualization, and DAX calculations. The actionable insights derived from the analysis can drive informed business decisions and strategic planning.
For more details or to discuss how I can contribute to your team, please connect with me on LinkedIn or contact me at abigailmwnz@gmail.com.